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src/cluster.c  view on Meta::CPAN

    for (i = 0; i < nelements; i++) index[i] = i;
    for (i = 0; i < nelements; i++) {
        j = (int) (i + (nelements-i)*uniform());
        ix = index[j];
        index[j] = index[i];
        index[i] = ix;
    }

    /* Start the iteration */
    for (iter = 0; iter < niter; iter++) {
        int ixbest = 0;
        int iybest = 0;
        int iobject = iter % nelements;
        iobject = index[iobject];
        if (transpose == 0) {
            double closest = metric(ndata, data, celldata[ixbest], mask,
                                    dummymask, weights, iobject, iybest,
                                    transpose);
            double radius = maxradius * (1. - ((double)iter)/((double)niter));
            double tau = inittau * (1. - ((double)iter)/((double)niter));

            for (ix = 0; ix < nxgrid; ix++) {
                for (iy = 0; iy < nygrid; iy++) {
                    double distance = metric(ndata, data, celldata[ix], mask,
                                             dummymask, weights, iobject, iy,
                                             transpose);
                    if (distance < closest) {
                        ixbest = ix;
                        iybest = iy;
                        closest = distance;
                    }
                }
            }
            for (ix = 0; ix < nxgrid; ix++) {
                for (iy = 0; iy < nygrid; iy++) {
                    if (sqrt((ix-ixbest)*(ix-ixbest)+(iy-iybest)*(iy-iybest)) <
                        radius) {
                        double sum = 0.;
                        for (i = 0; i < ndata; i++) {
                            if (mask[iobject][i] == 0) continue;
                            celldata[ix][iy][i] +=
                                tau * (data[iobject][i]/stddata[iobject]
                                      -celldata[ix][iy][i]);
                        }
                        for (i = 0; i < ndata; i++) {
                            double term = celldata[ix][iy][i];

src/cluster.c  view on Meta::CPAN

                }
            }
        }
        else {
            double closest;
            double** celldatavector = malloc(ndata*sizeof(double*));
            double radius = maxradius * (1. - ((double)iter)/((double)niter));
            double tau = inittau * (1. - ((double)iter)/((double)niter));

            for (i = 0; i < ndata; i++)
                celldatavector[i] = &(celldata[ixbest][iybest][i]);
            closest = metric(ndata, data, celldatavector, mask, dummymask,
                             weights, iobject, 0, transpose);
            for (ix = 0; ix < nxgrid; ix++) {
                for (iy = 0; iy < nygrid; iy++) {
                    double distance;
                    for (i = 0; i < ndata; i++)
                        celldatavector[i] = &(celldata[ixbest][iybest][i]);
                    distance = metric(ndata, data, celldatavector, mask,
                                      dummymask, weights, iobject, 0,
                                      transpose);
                    if (distance < closest) {
                        ixbest = ix;
                        iybest = iy;
                        closest = distance;
                    }
                }
            }
            free(celldatavector);
            for (ix = 0; ix < nxgrid; ix++) {
                for (iy = 0; iy < nygrid; iy++) {
                    if (sqrt((ix-ixbest)*(ix-ixbest)+(iy-iybest)*(iy-iybest)) <
                        radius) {
                        double sum = 0.;
                        for (i = 0; i < ndata; i++) {
                            if (mask[i][iobject] == 0) continue;
                            celldata[ix][iy][i] +=
                                tau * (data[i][iobject]/stddata[iobject]
                                      -celldata[ix][iy][i]);
                        }
                        for (i = 0; i < ndata; i++) {
                            double term = celldata[ix][iy][i];

src/cluster.c  view on Meta::CPAN

    double (*metric) (int, double**, double**, int**, int**,
                      const double[], int, int, int) = setmetric(dist);

    if (transpose == 0) {
        int** dummymask = malloc(nygrid*sizeof(int*));
        for (i = 0; i < nygrid; i++) {
            dummymask[i] = malloc(ncolumns*sizeof(int));
            for (j = 0; j < ncolumns; j++) dummymask[i][j] = 1;
        }
        for (i = 0; i < nrows; i++) {
            int ixbest = 0;
            int iybest = 0;
            double closest = metric(ndata, data, celldata[ixbest], mask,
                                    dummymask, weights, i, iybest, transpose);
            int ix, iy;
            for (ix = 0; ix < nxgrid; ix++) {
                for (iy = 0; iy < nygrid; iy++) {
                    double distance = metric(ndata, data, celldata[ix], mask,
                                             dummymask, weights, i, iy,
                                             transpose);
                    if (distance < closest) {
                        ixbest = ix;
                        iybest = iy;
                        closest = distance;
                    }
                }
            }
            clusterid[i][0] = ixbest;
            clusterid[i][1] = iybest;
        }
        for (i = 0; i < nygrid; i++) free(dummymask[i]);
        free(dummymask);
    }
    else {
        double** celldatavector = malloc(ndata*sizeof(double*));
        int** dummymask = malloc(nrows*sizeof(int*));
        int ixbest = 0;
        int iybest = 0;
        for (i = 0; i < nrows; i++) {
            dummymask[i] = malloc(sizeof(int));
            dummymask[i][0] = 1;
        }
        for (i = 0; i < ncolumns; i++) {
            double closest;
            int ix, iy;
            for (j = 0; j < ndata; j++)
                celldatavector[j] = &(celldata[ixbest][iybest][j]);
            closest = metric(ndata, data, celldatavector, mask, dummymask,
                             weights, i, 0, transpose);
            for (ix = 0; ix < nxgrid; ix++) {
                for (iy = 0; iy < nygrid; iy++) {
                    double distance;
                    for (j = 0; j < ndata; j++)
                        celldatavector[j] = &(celldata[ix][iy][j]);
                    distance = metric(ndata, data, celldatavector, mask,
                                      dummymask, weights, i, 0, transpose);
                    if (distance < closest) {
                        ixbest = ix;
                        iybest = iy;
                        closest = distance;
                    }
                }
            }
            clusterid[i][0] = ixbest;
            clusterid[i][1] = iybest;
        }
        free(celldatavector);
        for (i = 0; i < nrows; i++) free(dummymask[i]);
        free(dummymask);
    }
}

/* ******************************************************************* */

void